Journal of King Saud University - Computer and Information Sciences,
Journal Year:
2023,
Volume and Issue:
35(10), P. 101846 - 101846
Published: Nov. 16, 2023
The
integration
of
AI
technology
with
digital
transformation
has
profoundly
shaped
the
evolution
towards
triplet
architecture,
grounded
in
human-centric
methodologies.
By
infusing
human
intellectual
activities
into
both
physical
and
cyberspace,
innovative
links
between
humans
machines
are
established.
Despite
limitations
transitioning
from
tangible
presence
to
realm
extensive
efforts
underway
harness
emotional,
visual,
oral
responses,
thereby
enhancing
reasoning
predictive
capabilities
twins.
These
advancements
aim
elevate
real-time
interactions
virtual
systems
by
integrating
intelligent
algorithms
cognitive
computing
This
paper
meticulously
analyses
recent
trends
twins,
tracing
their
traditional
concepts
applications
a
nuanced
hierarchy
that
incorporates
intuition,
knowledge,
creativity
within
cyberspace.
we
delve
hierarchical
framework
triplet,
resonating
maturity,
domination,
volition
levels,
enhances
perceptual
study
provides
systematic
overview
development
ultra-realistic
models,
incorporating
data-driven
artefacts
integrate
multidomain,
multiphysics,
multiscale
simulations.
research
scope
is
focused
on
augmenting
perceptive
heuristic
utilizing
data
analytics,
retrieving
heterogeneous
entities
using
semantic
artificial
intelligence
technologies,
amalgamating
machine
learning
insight
knowledge.
proposed
aims
enhance
cyberspace's
capacity
for
learning,
skills,
knowledge
transfer.
It
can
be
guideline
researcher
promote
augmentation
brain
through
brain-machine/computer
interface,
virtual,
augmented,
extended
reality,
fostering
symbiotic
relationship
industrial
metaverse
industry
5.0.
discusses
future
directions
challenges
involved
developing
twins
paradigm,
aiming
embody
human-machine
symbiosis.
Journal of Cloud Computing Advances Systems and Applications,
Journal Year:
2024,
Volume and Issue:
13(1)
Published: Aug. 15, 2024
The
concept
of
the
Human
Digital
Twin
(HDT)
has
recently
emerged
as
a
new
research
area
within
domain
digital
twin
technology.
HDT
refers
to
replica
physical-world
human
in
world.
Currently,
on
is
still
its
early
stages,
with
lack
comprehensive
and
in-depth
analysis
from
perspectives
universal
frameworks,
core
technologies,
applications.
Therefore,
this
paper
conducts
an
extensive
literature
review
research,
analyzing
underlying
technologies
establishing
typical
frameworks
which
functions
or
components
are
organized.
Based
findings
aforementioned
work,
proposes
generic
architecture
for
system
describes
function
blocks
corresponding
technologies.
Subsequently,
presents
state
art
their
applications
healthcare,
industry,
daily
life
domains.
Finally,
discusses
various
issues
related
development
points
out
trends
challenges
future
development.
Recent
years
have
witnessed
numerous
technical
breakthroughs
in
connected
and
autonomous
vehicles
(CAVs).
On
the
one
hand,
these
significantly
advanced
development
of
intelligent
transportation
systems
(ITSs);
on
other
new
traffic
participants
introduce
more
complex
uncertain
elements
to
ITSs
from
social
space.
Digital
twins
(DTs)
provide
real-time,
data-driven,
precise
modeling
for
constructing
digital
mapping
physical-world
ITSs.
Meanwhile,
metaverse
integrates
emerging
technologies
such
as
virtual
reality/mixed
reality,
artificial
intelligence,
DTs
model
explore
how
realize
improved
sustainability,
increased
efficiency,
enhanced
safety.
More
recently,
a
leading
effort
toward
general
concept
foundation
was
proposed
has
achieved
significant
success,
showing
great
potential
lay
cornerstone
diverse
intelligence
applications
across
different
domains.
In
this
article,
we
big
models
embodied
parallel
driving
cyber-physical-social
spaces,
which
integrate
construct
training
space
CAVs,
present
comprehensive
elucidation
crucial
characteristics
operational
mechanisms.
Beyond
providing
infrastructure
driving,
article
also
discusses
future
trends
research
directions,
"6S"
goals
driving.
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 30, 2025
Abstract
Predicting
repetitive
transcranial
magnetic
stimulation
(rTMS)
effects
on
whole-brain
dynamics
in
clinical
populations
is
crucial
for
developing
personalized
therapies
and
advancing
precision
medicine
brain
disorders.
This
study
provides
the
first
proof-of-concept
demonstrating
that
Digital
Twin
Brain
(DTB)
can
forecast
rTMS
state
individuals
with
disorders
(chronic
tinnitus).
First,
we
identified
two
aberrant
states
predominantly
overlapped
somatomotor
default
mode
networks,
respectively.
Subsequently,
developed
DTB
patients
derived
regional
responses
each
region,
revealing
distinct
roles
of
parieto-occipital
frontal
regions.
Mechanistically,
examined
biological
plausibility
using
tinnitus-specific
risk
genes
investigated
multi-scale
neurobiological
relevance.
Clinically,
found
predict
an
independent,
longitudinal
dataset
(all
r
>
0.78).
Particularly,
predictive
capacity
exhibits
a
state-specific
nature.
Overall,
this
work
proposes
novel
DTB-based
framework
predicting
empirical
evidence
supporting
its
utility.
approach
may
be
generalizable
to
other
neuromodulation
techniques,
promoting
broader
advancements
health.
